Load Forecasting
36 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Load Forecasting
ASHRAE energy prediction III
PJM(AEP)
Latest papers
Benchmarks and Custom Package for Electrical Load Forecasting
leo-vk/proenfo
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Based on this, we conducted extensive experiments on load data at different levels, providing a reference for researchers to compare different load forecasting models.
BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load Forecasting
nrel/buildingsbench
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NeurIPS 2023
We also show that fine-tuning pretrained models on real commercial and residential buildings improves performance for a majority of target buildings.
Transformer Training Strategies for Forecasting Multiple Load Time Series
kit-iai/transformer-training-strategies
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We evaluate whether a Transformer load forecasting model benefits from a transfer learning strategy, where a global univariate model is trained on the load time series from multiple clients.
A Unifying Framework of Attention-based Neural Load Forecasting
In this paper, we propose a unifying deep learning framework for load forecasting, which includes time-varying feature weighting, hierarchical temporal attention, and feature-reinforced error correction.
Short-Term Aggregated Residential Load Forecasting using BiLSTM and CNN-BiLSTM
Using a publicly available dataset consisting of 38 homes, the BiLSTM and CNN-BiLSTM models are trained to forecast the aggregated active power demand for each hour within a 24 hr.
Transfer Learning in Deep Learning Models for Building Load Forecasting: Case of Limited Data
shomerthesec/Research_timeseries_forecasting_using_ashrae_dataset
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In order to adapt Deep Learning models for buildings with limited and scarce data, this paper proposes a Building-to-Building Transfer Learning framework to overcome the problem and enhance the performance of Deep Learning models.
Availability Adversarial Attack and Countermeasures for Deep Learning-based Load Forecasting
To tackle this attack, an adversarial training algorithm is proposed.
Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load Forecasting
slaweks17/es-adrnn-with-context
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These cells enable the model to capture short-term, long-term and seasonal dependencies across time series as well as to weight dynamically the input information.
Optimal Adaptive Prediction Intervals for Electricity Load Forecasting in Distribution Systems via Reinforcement Learning
Case studies on both load and net load demonstrate that the proposed method can better adapt to data distribution compared with online central PIs method.
Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows
The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level to increase efficiency and ensure reliable control.
